Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Metabolic labeling of secreted matrix to investigate cell–material interactions in tissue engineering and mechanobiology

Abstract

Re-creating features of the native extracellular matrix (ECM) with engineered biomaterials has become a valuable tool to probe the influence of ECM properties on cellular functions (e.g., differentiation) and toward the engineering of tissues. However, characterization of newly secreted (nascent) matrix and turnover, which are important in the context of cells interacting with these biomaterials, has been limited by a lack of tools. We developed a protocol to visualize and quantify the spatiotemporal evolution of newly synthesized and deposited matrix by cells that are either cultured atop (2D) or embedded within (3D) biomaterial systems (e.g., hydrogels, fibrous matrices). This technique relies on the incorporation of a noncanonical amino acid (azidohomoalanine) into proteins as they are synthesized. Deposited nascent ECM components are then visualized with fluorescent cyclooctynes via copper-free cycloaddition for spatiotemporal analysis or modified with cleavable biotin probes for identification. Here we describe the preparation of hyaluronic acid hydrogels through ultraviolet or visible light induced cross-linking for 2D and 3D cell culture, as well as the fluorescent labeling of nascent ECM deposited by cells during culture. We also provide protocols for secondary immunofluorescence of specific ECM components and ImageJ-based ECM quantification methods. Hyaluronic acid polymer synthesis takes 2 weeks to complete, and hydrogel formation for 2D or 3D cell culture is performed in 2–3 h. Lastly, we detail the identification of nascent proteins, including enrichment, preparation and analysis with mass spectrometry, which can be completed in 10 d.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the nascent matrix labeling approach.
Fig. 2: Application of metabolic labeling to monitor nascent protein accumulation at the cell–hydrogel interface in 3D hydrogels.
Fig. 3: Application of nascent protein labeling to assess cell mechanobiologic response atop 2D biomaterials.
Fig. 4: Application of nascent protein labeling to probe mechanosensing and spreading in 3D hydrogels.
Fig. 5: Enrichment of AHA-labeled proteins within engineered hydrogels in vitro and from murine embryos in vivo.
Fig. 6: Hydrogel preparation for 2D and 3D cell culture studies.
Fig. 7: Quantification of nascent matrix thickness in ImageJ.

Similar content being viewed by others

Data availability

All the data generated or analyzed during this study are included within this article or references cited and the Supplementary Information. Proteomics data are available in the MassIVE repository (https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=11535f3c322649d88e9225d42f5dd9d3). Additional information is available from the corresponding author on request.

References

  1. Gjorevski, N. & Nelson, C. M. Bidirectional extracellular matrix signaling during tissue morphogenesis. Cytokine Growth Factor Rev. 20, 459–465 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Herrera, J., Henke, C. A. & Bitterman, P. B. Extracellular matrix as a driver of progressive fibrosis. J. Clin. Invest. 128, 45–53 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Frantz, C., Stewart, K. M. & Weaver, V. M. The extracellular matrix at a glance. J. Cell Sci. 123, 4195–4200 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Birk, D. E., Hahn, R. A., Linsenmayer, C. Y. & Zycband, E. I. Characterization of collagen fibril segments from chicken embryo cornea, dermis and tendon. Matrix Biol. 15, 111–118 (1996).

    Article  CAS  PubMed  Google Scholar 

  5. Griffith, L. G. & Swartz, M. A. Capturing complex 3D tissue physiology in vitro. Nat. Rev. Mol. Cell Biol. 7, 211–224 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. Caliari, S. R. & Burdick, J. A. A practical guide to hydrogels for cell culture. Nat. Methods 13, 405–414 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Loebel, C., Rodell, C. B., Chen, M. H. & Burdick, J. A. Shear-thinning and self-healing hydrogels as injectable therapeutics and for 3D-printing. Nat. Protoc. 12, 1521–1541 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Prince, E. & Kumacheva, E. Design and applications of man-made biomimetic fibrillar hydrogels. Nat. Rev. Mater. 4, 99–115 (2019).

    Article  Google Scholar 

  9. Tomasek, J. J., Gabbiani, G., Hinz, B., Chaponnier, C. & Brown, R. A. Myofibroblasts and mechano-regulation of connective tissue remodelling. Nat. Rev. Mol. Cell Biol. 3, 349–363 (2002).

    Article  CAS  PubMed  Google Scholar 

  10. Burla, F., Mulla, Y., Vos, B. E., Aufderhorst-Roberts, A. & Koenderink, G. H. From mechanical resilience to active material properties in biopolymer networks. Nat. Rev. Phys. 1, 249–263 (2019).

    Article  Google Scholar 

  11. Storm, C., Pastore, J. J., MacKintosh, F. C., Lubensky, T. C. & Janmey, P. A. Nonlinear elasticity in biological gels. Nature 435, 191–194 (2005).

    Article  CAS  PubMed  Google Scholar 

  12. Matera, D. L. et al. Microengineered 3D pulmonary interstitial mimetics highlight a critical role for matrix degradation in myofibroblast differentiation. Sci. Adv. https://doi.org/10.1126/sciadv.abb5069 (2020).

  13. Davidson, M. D., Burdick, J. A. & Wells, R. G. Engineered biomaterial platforms to study fibrosis. Adv. Healthc. Mater. https://doi.org/10.1002/adhm.201901682 (2020).

  14. Davidson, M. D. et al. Programmable and contractile materials through cell encapsulation in fibrous hydrogel assemblies. Sci. Adv. 7, eabi8157 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ma, P. X. Biomimetic materials for tissue engineering. Adv. Drug Deliv. Rev. 60, 184–198 (2008).

    Article  CAS  PubMed  Google Scholar 

  16. Saleh, A. M., Wilding, K. M., Calve, S., Bundy, B. C. & Kinzer-Ursem, T. L. Non-canonical amino acid labeling in proteomics and biotechnology. J. Biol. Eng. 13, 43 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Dieterich, D. C. et al. In situ visualization and dynamics of newly synthesized proteins in rat hippocampal neurons. Nat. Neurosci. 13, 897–905 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Dieterich, D. C., Link, A. J., Graumann, J., Tirrell, D. A. & Schuman, E. M. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc. Natl Acad. Sci. USA 103, 9482–9487 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Nessen, M. A. et al. Selective enrichment of azide-containing peptides from complex mixtures. J. Proteome Res. 8, 3702–3711 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. van Hest, J. C. M., Kiick, K. L. & Tirrell, D. A. Efficient incorporation of unsaturated methionine analogues into proteins in vivo. J. Am. Chem. Soc. 122, 1282–1288 (2000).

    Article  CAS  Google Scholar 

  21. Beatty, K. E. et al. Fluorescence visualization of newly synthesized proteins in mammalian cells. Angew. Chem., Int. Ed. 45, 7364–7367 (2006).

    Article  CAS  Google Scholar 

  22. Zhang, J., Wang, J., Ng, S., Lin, Q. & Shen, H.-M. Development of a novel method for quantification of autophagic protein degradation by AHA labeling. Autophagy 10, 901–912 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhang, M. M., Tsou, L. K., Charron, G., Raghavan, A. S. & Hang, H. C. Tandem fluorescence imaging of dynamic S-acylation and protein turnover. P. Natl Acad. Sci. USA 107, 8627–8632 (2010).

    Article  CAS  Google Scholar 

  24. Adelmund, S. M., Ruskowitz, E. R., Farahani, P. E., Wolfe, J. V. & DeForest, C. A. Light-activated proteomic labeling via photocaged bioorthogonal non-canonical amino acids. ACS Chem. Biol. 13, 573–577 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Alvarez-Castelao, B. et al. Cell-type-specific metabolic labeling of nascent proteomes in vivo. Nat. Biotechnol. 35, 1196–1201 (2017).

    Article  CAS  PubMed  Google Scholar 

  26. Alvarez-Castelao, B., Schanzenbächer, C. T., Langer, J. D. & Schuman, E. M. Cell-type-specific metabolic labeling, detection and identification of nascent proteomes in vivo. Nat. Protoc. 14, 556–575 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Liu, Y. et al. Application of bio-orthogonal proteome labeling to cell transplantation and heterochronic parabiosis. Nat. Commun. 8, 643 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Sadlowski, C. et al. Graphene-based biosensor for on-chip detection of bio-orthogonally labeled proteins to identify the circulating biomarkers of aging during heterochronic parabiosis. Lab Chip 18, 3230–3238 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bonnevie, E. D. et al. Aberrant mechanosensing in injured intervertebral discs as a result of boundary-constraint disruption and residual-strain loss. Nat. Biomed. Eng. 3, 998–1008 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Loebel, C., Mauck, R. L. & Burdick, J. A. Local nascent protein deposition and remodelling guide mesenchymal stromal cell mechanosensing and fate in three-dimensional hydrogels. Nat. Mater. 18, 883–891 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. McLeod, C. M. & Mauck, R. L. High fidelity visualization of cell-to-cell variation and temporal dynamics in nascent extracellular matrix formation. Sci. Rep. 6, 38852 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Saleh, A. M., Jacobson, K. R., Kinzer-Ursem, T. L. & Calve, S. Dynamics of non-canonical amino acid-labeled intra- and extracellular proteins in the developing mouse. Cell Mol. Bioeng. 12, 495–509 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Calve, S., Witten, A. J., Ocken, A. R. & Kinzer-Ursem, T. L. Incorporation of non-canonical amino acids into the developing murine proteome. Sci. Rep. 6, 32377 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Loebel, C. et al. Metabolic labeling to probe the spatiotemporal accumulation of matrix at the chondrocyte–hydrogel interface. Adv. Funct. Mater. 30, 1909802 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tan, A. R. & Hung, C. T. Concise review: mesenchymal stem cells for functional cartilage tissue engineering: taking cues from chondrocyte-based constructs. Stem Cells Transl. Med. 6, 1295–1303 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Chen, F. H., Rousche, K. T. & Tuan, R. S. Technology Insight: adult stem cells in cartilage regeneration and tissue engineering. Nat. Clin. Pract. Rheumatol. 2, 373–382 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Vega, S. L., Kwon, M. Y. & Burdick, J. A. Recent advances in hydrogels for cartilage tissue engineering. Eur. Cell Mater. 33, 59–75 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Eslahi, N., Abdorahim, M. & Simchi, A. Smart polymeric hydrogels for cartilage tissue engineering: a review on the chemistry and biological functions. Biomacromolecules 17, 3441–3463 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Huebsch, N. Translational mechanobiology: designing synthetic hydrogel matrices for improved in vitro models and cell-based therapies. Acta Biomater. 94, 97–111 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Caliari, S. R., Vega, S. L., Kwon, M., Soulas, E. M. & Burdick, J. A. Dimensionality and spreading influence MSC YAP/TAZ signaling in hydrogel environments. Biomaterials 103, 314–323 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Yang, B. et al. Enhanced mechanosensing of cells in synthetic 3D matrix with controlled biophysical dynamics. Nat. Commun. 12, 3514 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Uitto, J. Biochemistry of the elastic fibers in normal connective tissues and its alterations in diseases. J. Invest. Dermatol. 72, 1–10 (1979).

    Article  CAS  PubMed  Google Scholar 

  43. Yuet, K. P. et al. Cell-specific proteomic analysis in Caenorhabditis elegans. Proc. Natl Acad. Sci. USA 112, 2705–2710 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Datta, D., Wang, P., Carrico, I. S., Mayo, S. L. & Tirrell, D. A. A designed phenylalanyl-tRNA synthetase variant allows efficient in vivo incorporation of aryl ketone functionality into proteins. J. Am. Chem. Soc. 124, 5652–5653 (2002).

    Article  CAS  PubMed  Google Scholar 

  45. Dunham, C., Havlioglu, N., Chamberlain, A., Lake, S. & Meyer, G. Adipose stem cells exhibit mechanical memory and reduce fibrotic contracture in a rat elbow injury model. FASEB J. 34, 12976–12990 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. Jowett, G. M. et al. ILC1 drive intestinal epithelial and matrix remodelling. Nat. Mater. 20, 250–259 (2021).

    Article  CAS  PubMed  Google Scholar 

  47. Norman, M. D. A., Ferreira, S. A., Jowett, G. M., Bozec, L. & Gentleman, E. Measuring the elastic modulus of soft culture surfaces and three-dimensional hydrogels using atomic force microscopy. Nat. Protoc. 16, 2418–2449 (2021).

    Article  CAS  PubMed  Google Scholar 

  48. Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell Proteom. 11, M111.014647 (2012).

    Article  CAS  Google Scholar 

  49. Gramlich, W. M., Kim, I. L. & Burdick, J. A. Synthesis and orthogonal photopatterning of hyaluronic acid hydrogels with thiol-norbornene chemistry. Biomaterials 34, 9803–9811 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Shih, H. & Lin, C.-C. Cross-linking and degradation of step-growth hydrogels formed by thiol–ene photoclick chemistry. Biomacromolecules 13, 2003–2012 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Aimetti, A. A., Machen, A. J. & Anseth, K. S. Poly(ethylene glycol) hydrogels formed by thiol-ene photopolymerization for enzyme-responsive protein delivery. Biomaterials 30, 6048–6054 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Mũnoz, Z., Shih, H. & Lin, C.-C. Gelatin hydrogels formed by orthogonal thiol–norbornene photochemistry for cell encapsulation. Biomater. Sci. 2, 1063–1072 (2014).

    Article  PubMed  Google Scholar 

  53. Loebel, C. et al. Cross-linking chemistry of tyramine-modified hyaluronan hydrogels alters mesenchymal stem cell early attachment and behavior. Biomacromolecules 18, 855–864 (2017).

    Article  CAS  PubMed  Google Scholar 

  54. Vega, S. L. et al. Combinatorial hydrogels with biochemical gradients for screening 3D cellular microenvironments. Nat. Commun. 9, 614 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Cosgrove, B. D. et al. N-cadherin adhesive interactions modulate matrix mechanosensing and fate commitment of mesenchymal stem cells. Nat. Mater. 15, 1297–1306 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Doube, M. et al. BoneJ: free and extensible bone image analysis in ImageJ. Bone 47, 1076–1079 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Mauck, R. L., Yuan, X. & Tuan, R. S. Chondrogenic differentiation and functional maturation of bovine mesenchymal stem cells in long-term agarose culture. Osteoarthr. Cartil. 14, 179–189 (2006).

    Article  CAS  Google Scholar 

  58. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  PubMed  Google Scholar 

  59. Perkins, D. N., Pappin, D. J., Creasy, D. M. & Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).

    Article  CAS  PubMed  Google Scholar 

  60. Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994).

    Article  CAS  PubMed  Google Scholar 

  61. Catterall, J. B. et al. Protein modification by deamidation indicates variations in joint extracellular matrix turnover. J. Biol. Chem. 287, 4640–4651 (2012).

    Article  CAS  PubMed  Google Scholar 

  62. Naba, A. et al. Characterization of the extracellular matrix of normal and diseased tissues using proteomics. J. Proteome Res. 16, 3083–3091 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Jonkman, J., Brown, C. M., Wright, G. D., Anderson, K. I. & North, A. J. Tutorial: guidance for quantitative confocal microscopy. Nat. Protoc. https://doi.org/10.1038/s41596-020-0313-9 (2020).

Download references

Acknowledgements

The authors are grateful for financial support from the National Institutes of Health (R01 AR077362, R01 AR056624, R01 AR071359 and K99 HL151670), as well as the Center for Engineering MechanoBiology through the National Science Foundation’s STC Program (CMMI 15-48571), and technical support from Christopher Ebmeier of the ‘Central Analytical Mass Spectrometry Facility and W.M. Keck Foundation Proteomics Resource’ at the University of Colorado Boulder. The authors thank C.M. McLeod, E.D. Bonnevie and J.H. Galarraga for helpful discussions.

Author information

Authors and Affiliations

Authors

Contributions

C.L., A.M.S., K.R.J. and R.D. performed the experiments and analyzed the data. All authors wrote the manuscript, and R.L.M., S.C. and J.A.B. supervised the research.

Corresponding author

Correspondence to Jason A. Burdick.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Protocols thanks Manuel Mayr and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Loebel, C. et. al. Nat. Mater. 18, 883–891 (2019): https://doi.org/10.1038/s41563-019-0307-6

McLeod, C. M. & Mauck, R. L. Sci. Rep. 6, 38852 (2016): https://doi.org/10.1038/srep38852

Bonnevie, E. D. et al. Nat. Biomed. Eng. 3, 998–1008 (2019): https://doi.org/10.1038/s41551-019-0458-4

Loebel, C. et. al. Adv. Funct. Mater. 30, 1909802 (2020): https://doi.org/10.1002/adfm.201909802

Saleh, A. M. et al. Cell Mol. Bioeng. 12, 495–509 (2019): https://doi.org/10.1007/s12195-019-00592-1

Key data used in this protocol

Loebel, C. et. al. Nat. Mater. 18, 883–891 (2019): https://doi.org/10.1038/s41563-019-0307-6

Saleh, A. M. et al. Cell Mol. Bioeng. 12, 495–509 (2019): https://doi.org/10.1007/s12195-019-00592-1

Extended data

Extended Data Fig. 1 Viability of cells cultured in different concentrations of AHA.

Chondrocytes were cultured for 7 days within nondegradable hyaluronic acid hydrogels in chondrogenic media supplemented with different concentrations of AHA and cell viability quantified through live-dead staining (n = 4 hydrogels, mean ± SD, **p ≤ 0.01, by one-way ANOVA with Bonferroni post hoc).

Extended Data Fig. 2 Comparison of nascent protein labeling after and before cell fixation.

Representative images of a maximum intensity z-projection of methionine containing nascent proteins labeled with fluorophore-conjugated cyclooctynes (DBCO-488) after (left) and before (right) adding the fixative. Cells were cultured for 3 days within MMP-degradable hyaluronic acid (HA) hydrogels (media supplemented with azidohomoalanine, AHA). Red lines indicate cell boundaries identified with cell membrane staining. Scale bar, 20 µm. Adapted with permission from ref. 30.

Extended Data Fig. 3 Quantification of nascent matrix volume and area in ImageJ.

a Quantification of nascent matrix volume: Acquire z-stack confocal images of the nascent ECM and cell membrane, transform into 3D object, and split the channels into single 3D images using ImageJ (i). To obtain an image of the nascent matrix only, adjust the threshold for each channel with ‘Otsu thresholding’ and subtract the ‘cell’ image from the ‘nascent matrix’ image (ii). Use the ImageJ ‘3D object counter’ function to measure the nascent matrix volume (iii). Scale bars, 50 µm. b Quantification of nascent matrix area: Acquire z-stack confocal images of the nascent matrix and cell membrane, and split the channels into single z-stack images using ImageJ (i). To obtain an image of the nascent matrix only, adjust the threshold for each channel with ‘Otsu thresholding’ and subtract the ‘cell’ image from the ‘nascent matrix’ image (ii). Use the ImageJ ‘Analyze particles’ function in max projection to measure the nascent matrix area (iii). Scale bars, 50 µm.

Extended Data Fig. 4 Analysis of newly synthesized proteome deposited by chondrocytes within 3D hydrogels.

a Western blot analysis of AHA-labeled and control samples cultured in methionine containing media (Met) showing the influence of iodoacetamide (IAA), sodium ascorbate (SA) and aminoguanidine (AG) at varied concentrations on the efficiency of the click reaction and unspecific labeling. b Western blot analysis of AHA-labeled and Met samples showing the influence of resin to protein ratio on the enrichment and nonspecific binding of labeled proteins by NeutrAvidin affinity purification.

Extended Data Fig. 5 Parameter settings for identification of newly synthesized proteins.

MaxQuant and instrument parameters for identification and quantification.

Extended Data Fig. 6 Diagram illustrating the classification of proteins.

Description of how to classify proteins as cytosolic, nuclear, membrane, cytoskeletal or matrisome-related based on the Gene Ontology Consortium and the Matrisome Project.

Extended Data Fig. 7 Analysis of protein and peptide concentrations during sample preparation.

a Measured protein concentrations using the Pierce Coomassie (Bradford) Protein Assay at step 59 (Start), step 69 (click-reac.) and step 83 (cleaved), n = 6, mean ± SD, color-coded individual points represent one biological replicate. b Measured peptide concentrations using the Pierce Quantitative Fluorometric Peptide Assay at step 88 (Trypsin/LysC), step 89 (SDS removal) and step 94 (C18 clean-up), n = 9, mean ± SD, color-coded individual points represent one biological replicate.

Supplementary information

Supplementary Information

Supplementary Method: LC-MS/MS analysis.

Reporting Summary

Supplementary Table 1

Identification and analysis of newly synthesized proteins. proteinGroups (the Original ‘proteinGroups’ data generated by MaxQuant), Filtered Proteins (Filtered protein IDs and intensities used for downstream data analysis) and Data Analyses (ECM proteins identified with fold change calculations and annotations).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Loebel, C., Saleh, A.M., Jacobson, K.R. et al. Metabolic labeling of secreted matrix to investigate cell–material interactions in tissue engineering and mechanobiology. Nat Protoc 17, 618–648 (2022). https://doi.org/10.1038/s41596-021-00652-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-021-00652-9

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing